{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:29:22Z","timestamp":1772252962208,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,3,4]],"date-time":"2020-03-04T00:00:00Z","timestamp":1583280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Early detection of cancer increases the probability of recovery. This paper presents an intelligent decision support system (IDSS) for the early diagnosis of cancer based on gene expression profiles collected using DNA microarrays. Such datasets pose a challenge because of the small number of samples (no more than a few hundred) relative to the large number of genes (in the order of thousands). Therefore, a method of reducing the number of features (genes) that are not relevant to the disease of interest is necessary to avoid overfitting. The proposed methodology uses the information gain (IG) to select the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the grey wolf optimization (GWO) algorithm. Finally, the methodology employs a support vector machine (SVM) classifier for cancer type classification. The proposed methodology was applied to two datasets (Breast and Colon) and was evaluated based on its classification accuracy, which is the most important performance measure in disease diagnosis. The experimental results indicate that the proposed methodology is able to enhance the stability of the classification accuracy as well as the feature selection.<\/jats:p>","DOI":"10.3390\/sym12030408","type":"journal-article","created":{"date-parts":[[2020,3,4]],"date-time":"2020-03-04T10:46:08Z","timestamp":1583318768000},"page":"408","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["Breast and Colon Cancer Classification from Gene Expression Profiles Using Data Mining Techniques"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3849-4566","authenticated-orcid":false,"given":"Mohamed Loey Ramadan","family":"AbdElNabi","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Computers Artificial Intelligence, Benha University, Benha 13511, Egypt"}]},{"given":"Mohammed","family":"Wajeeh Jasim","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computers Artificial Intelligence, Benha University, Benha 13511, Egypt"}]},{"given":"Hazem","family":"M. EL-Bakry","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computer &amp; Information Sciences, Mansoura University, Mansoura 35511, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0200-2918","authenticated-orcid":false,"given":"Mohamed","family":"Hamed N. Taha","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Faculty of Computers &amp; Artificial Intelligence, Cairo University, Cairo 12613, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8614-9057","authenticated-orcid":false,"given":"Nour Eldeen","family":"M. Khalifa","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Faculty of Computers &amp; Artificial Intelligence, Cairo University, Cairo 12613, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Walker, D., Bendel, A., Stiller, C., Indelicato, D., Smith, S., Murray, M., and Bleyer, A. (2017). Central Nervous System Tumors. Pediatric Oncology, Springer.","DOI":"10.1007\/978-3-319-33679-4_14"},{"key":"ref_2","unstructured":"Cancer.net (2020, January 01). American Society of Clinical Oncology (ASCO). Available online: https:\/\/www.cancer.net\/cancer-types\/central-nervous-system-childhood\/view-all."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tan, Y., Shi, Y., and Tan, K.C. (2010, January 12\u201315). Intelligent Decision Support System for Breast Cancer. Proceedings of the Advances in Swarm Intelligence, Beijing, China.","DOI":"10.1007\/978-3-642-13498-2"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21590","article-title":"Cancer statistics, 2020","volume":"70","author":"Siegel","year":"2020","journal-title":"CA A Cancer J. Clin."},{"key":"ref_5","unstructured":"Al-Badareen, A.B., Selamat, M.H., Samat, M.H., Nazira, Y., and Akkanat, O. (2020, January 01). A Review on Clinical Decision Support Systems in Healthcare. Available online: \/paper\/A-review-on-clinical-decision-support-systems-in-Al-Badareen-Selamat\/cb1e1c668f6e0def2f893b3669f5e9766033f258."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.compmedimag.2007.02.002","article-title":"Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential","volume":"31","author":"Doi","year":"2007","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ahsen, M.E., Boren, T.P., Singh, N.K., Misganaw, B., Mutch, D.G., Moore, K.N., Backes, F.J., McCourt, C.K., Lea, J.S., and Miller, D.S. (2017). Sparse feature selection for classification and prediction of metastasis in endometrial cancer. BMC Genomics, 18.","DOI":"10.1186\/s12864-017-3604-y"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1148\/radiol.2333031484","article-title":"Diagnostic Accuracy of Mammography, Clinical Examination, US, and MR Imaging in Preoperative Assessment of Breast Cancer","volume":"233","author":"Berg","year":"2004","journal-title":"Radiology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.jbi.2017.01.016","article-title":"Development of a two-stage gene selection method that incorporates a novel hybrid approach using the cuckoo optimization algorithm and harmony search for cancer classification","volume":"67","author":"Elyasigomari","year":"2017","journal-title":"J. Biomed. Inform."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.asoc.2016.11.026","article-title":"Classification of human cancer diseases by gene expression profiles","volume":"50","author":"Salem","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1007\/s10044-016-0574-7","article-title":"Early diagnosis of breast cancer by gene expression profiles","volume":"20","author":"Salem","year":"2017","journal-title":"Pattern Anal. Appl."},{"key":"ref_12","unstructured":"Bennet, J., Ganaprakasam, C., and Kumar, N. (2015). A hybrid approach for gene selection and classification using support vector machine. Int. Arab J. Inf. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yeh, J.-Y., Wu, T.-S., Wu, M.-C., and Chang, D.-M. (2007, January 21\u201323). Applying Data Mining Techniques for Cancer Classification from Gene Expression Data. Proceedings of the 2007 International Conference on Convergence Information Technology (ICCIT 2007), Gyeongju, South Korea.","DOI":"10.1109\/ICCIT.2007.153"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.patcog.2011.06.006","article-title":"An ensemble of filters and classifiers for microarray data classification","volume":"45","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gunavathi, C., and Premalatha, K. (2014). Performance Analysis of Genetic Algorithm with kNN and SVM for Feature Selection in Tumor Classification.","DOI":"10.1155\/2014\/693831"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bouazza, S.H., Hamdi, N., Zeroual, A., and Auhmani, K. (2015, January 25\u201326). Gene-expression-based cancer classification through feature selection with KNN and SVM classifiers. Proceedings of the 2015 Intelligent Systems and Computer Vision (ISCV), Fez, Morocco.","DOI":"10.1109\/ISACV.2015.7106168"},{"key":"ref_17","unstructured":"Abraham, A., Kr\u00f6mer, P., and Snasel, V. (2015, January 9\u201311). Feature Subset Selection Approach by Gray-Wolf Optimization. Proceedings of the Afro-European Conference for Industrial Advancement, Villejuif (Paris-sud), France."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.asoc.2017.01.046","article-title":"Gene selection for designing optimal fuzzy rule base classifier by estimating missing value","volume":"55","author":"Paul","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"198363","DOI":"10.1155\/2015\/198363","article-title":"A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data","volume":"2015","author":"Hira","year":"2015","journal-title":"Adv. Bioinform."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1945","DOI":"10.3390\/e13111945","article-title":"A Characterization of Entropy in Terms of Information Loss","volume":"13","author":"Baez","year":"2011","journal-title":"Entropy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5677","DOI":"10.3390\/e16115677","article-title":"A Load Balancing Algorithm Based on Maximum Entropy Methods in Homogeneous Clusters","volume":"16","author":"Chen","year":"2014","journal-title":"Entropy"},{"key":"ref_22","first-page":"395","article-title":"A Review on Feature Selection Methods For Classification Tasks","volume":"5","author":"Mwadulo","year":"2016","journal-title":"Int. J. Comput. Appl. Technol. Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Okun, O. (2011). Feature Selection and Ensemble Methods for Bioinformatics: Algorithmic Classification and Implementations.","DOI":"10.4018\/978-1-60960-557-5"},{"key":"ref_24","unstructured":"Bramer, M. (2007). Principles of Data Mining, Springer. Undergraduate Topics in Computer Science."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"(2014). Grey Wolf Optimizer. Adv. Eng. Softw., 69, 46\u201361.","DOI":"10.1016\/j.advengsoft.2013.12.007"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mech, L.D. (1999). Alpha Status, Dominance, and Division of Labor in Wolf Packs.","DOI":"10.1139\/z99-099"},{"key":"ref_27","unstructured":"Kumar, D.P.S., and Sathyadevi, G. (2011). Decision Support System for Medical Diagnosis Using Data Mining."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.beproc.2011.09.006","article-title":"Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations","volume":"88","author":"Muro","year":"2011","journal-title":"Behav. Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/415436a","article-title":"Prediction of central nervous system embryonal tumour outcome based on gene expression","volume":"415","author":"Pomeroy","year":"2002","journal-title":"Nature"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.soildyn.2015.04.004","article-title":"Grey Wolf Optimizer for parameter estimation in surface waves","volume":"75","author":"Song","year":"2015","journal-title":"Soil Dyn. Earthq. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.neucom.2015.06.083","article-title":"Binary grey wolf optimization approaches for feature selection","volume":"172","author":"Emary","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Marsland, S. (2014). Machine Learning: An Algorithmic Perspective, Chapman & Hall\/CRC. [2nd ed.].","DOI":"10.1201\/b17476"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7270","DOI":"10.1016\/j.eswa.2012.01.096","article-title":"Microarray gene expression classification with few genes: Criteria to combine attribute selection and classification methods","volume":"39","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_34","first-page":"561","article-title":"Informative Gene Selection for Cancer Classification with Microarray Data Using a Metaheuristic Framework","volume":"19","author":"Pyingkodi","year":"2018","journal-title":"Asian Pac. J. Cancer Prev."},{"key":"ref_35","unstructured":"Cho, S.-B., and Won, H.-H. (2003). Machine Learning in DNA Microarray Analysis for Cancer Classification. First Asia-Pacific Bioinformatics Conference on Bioinformatics 2003\u2014Volume 19, Australian Computer Society, Inc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1960","DOI":"10.1016\/j.patrec.2008.06.018","article-title":"Cross-validation and bootstrapping are unreliable in small sample classification","volume":"29","author":"Isaksson","year":"2008","journal-title":"Pattern Recognit. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4103\/jmss.JMSS_21_17","article-title":"Improving Classification of Cancer and Mining Biomarkers from Gene Expression Profiles Using Hybrid Optimization Algorithms and Fuzzy Support Vector Machine","volume":"8","author":"Moteghaed","year":"2018","journal-title":"J. Med. Signals Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1109\/TSMC.2017.2733545","article-title":"Subgraph Robustness of Complex Networks Under Attacks","volume":"49","author":"Shang","year":"2019","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/3\/408\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:04:04Z","timestamp":1760173444000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/3\/408"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,4]]},"references-count":38,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["sym12030408"],"URL":"https:\/\/doi.org\/10.3390\/sym12030408","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202002.0324.v1","asserted-by":"object"}]},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,4]]}}}